{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "467fb75b",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "#  18\\.  支持向量机实现人像分类  # \n",
    "\n",
    "##  18.1.  介绍  # \n",
    "\n",
    "支持向量机是一个非常优秀的算法。本次挑战中，我们将使用 scikit-learn 提供的支持向量机方法来完成人脸图像分类任务。 \n",
    "\n",
    "##  18.2.  知识点  # \n",
    "\n",
    "  * 图像数据预处理 \n",
    "\n",
    "  * 支持向量机分类 \n",
    "\n",
    "首先，我们通过 scikit-learn 提供的 ` fetch_lfw_people  ` 下载人脸数据集，数据集最初出自 [ Labeled Faces in the Wild ](http://vis-www.cs.umass.edu/lfw/) 项目。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d236511",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['Ariel Sharon', 'Colin Powell', 'Donald Rumsfeld', 'George W Bush',\n",
       "        'Gerhard Schroeder', 'Hugo Chavez', 'Junichiro Koizumi',\n",
       "        'Tony Blair'], dtype='<U17'),\n",
       " (1348, 62, 47))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_lfw_people\n",
    "\n",
    "# 加载数据集\n",
    "faces = fetch_lfw_people(min_faces_per_person=60)\n",
    "faces.target_names, faces.images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d92242d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "(array(['Ariel Sharon', 'Colin Powell', 'Donald Rumsfeld', 'George W Bush',\n",
    "        'Gerhard Schroeder', 'Hugo Chavez', 'Junichiro Koizumi',\n",
    "        'Tony Blair'], dtype='<U17'),\n",
    " (1348, 62, 47))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7132c7d",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "可以看的，我们仅使用了 8 位名人的人像数据，总共包含 1348 个样本。其中，每张人像照片的尺寸为  $62*47$  像素。总结 ` faces  ` 属性有： \n",
    "\n",
    "属性  |  描述   \n",
    "---|---  \n",
    "` faces.images  ` |  62x47 矩阵，记录人脸图像中的像素值   \n",
    "` faces.data  ` |  将 images 对应的 62x47 矩阵转换为行向量   \n",
    "` faces.target_names  ` |  8 位人像姓名   \n",
    "` faces.target  ` |  8 位人像依次编号   \n",
    "  \n",
    "下面，我们先使用 Matplotlib 绘图来预览这些数据。 \n",
    "\n",
    "Exercise 18.1 \n",
    "\n",
    "挑战：预览数据集前 5 张人像图片，以 1 行 5 列子图呈现。 \n",
    "\n",
    "规定：每张图片横轴上显示该张图片对应人像姓名。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8547a637",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "## 代码开始 ### (≈4 行代码)\n",
    "\n",
    "## 代码结束 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851e131e",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "参考答案  Exercise 18.1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "400f66ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n",
      " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair'] [1 3 3 ... 7 3 5]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "### 代码开始 ### (≈4 行代码)\n",
    "fig, axes = plt.subplots(1, 5, figsize=(12, 6))\n",
    "for i, image in enumerate(faces.images[:5]):\n",
    "    axes[i].imshow(image)\n",
    "    axes[i].set_xlabel(faces.target_names[faces.target[i]])\n",
    "### 代码结束 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "836d7aa4",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "\n",
    "由于图片本身为 2 维数组，需要处理之后才能用于训练模型。所以，下面我们使用 ` faces.data  ` 数据，其已经将每一个人像对于的二维数组展平成 1 维。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0849e7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1348, 2914)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "faces.data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e348986",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "可以看的， ` faces.data  ` 的形状为  $(1348, 2914)$  ，即代表有 1348 个样本，每一个样本对应了 2914 个特征。而这 2914 个特征即是将人像图片  $62*47 = 2914$  展平之后的向量。 \n",
    "\n",
    "接下来，按照惯例需要对数据集进行切分，将其分为训练集和测试集。不过，这里值的注意的是，由于样本只有 1348 个，而每个样本对应的特征则为 2914。机器学习建模过程中，我们要避免特征远大于样本数量的情形，这样训练出来的模型一般表现都会非常糟糕。 \n",
    "\n",
    "所以，这里我们需要对数据特征进行「降维」，实际上就是减少数据的特征量。这里使用到 PCA 降维方法，该方法会在后续实验中详细介绍，这里不做讲解。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4efa7396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1348, 150)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 直接运行，将数据特征缩减为 150 个\n",
    "pca = PCA(n_components=150, whiten=True, random_state=42)\n",
    "pca_data = pca.fit_transform(faces.data)\n",
    "pca_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbe04d8f",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "可以看的，数据形状又先前的  $(1348, 2914)$  变为  $(1348, 150)$  。 \n",
    "\n",
    "下面，就可以根据降维后的数据切分训练集和测试集了。 \n",
    "\n",
    "Exercise 18.2 \n",
    "\n",
    "挑战：使用 ` train_test_split()  ` 将数据集切分为 80%（训练集） 和 20%（测试集） 两部分。 \n",
    "\n",
    "规定：训练集特征，测试集特征，训练集目标，测试集目标分别为： ` X_train  ` , ` X_test  ` , ` y_train  ` , ` y_test  ` ，随机数种子定为 42。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e794cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "## 代码开始 ### (≈1 行代码)\n",
    "\n",
    "## 代码结束 ###\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752b92c8",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "参考答案  Exercise 18.2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16638f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1078, 150), (270, 150), (1078,), (270,))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "### 代码开始 ### (≈1 行代码)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    pca_data, faces.target, test_size=0.2, random_state=42)\n",
    "### 代码结束 ###\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
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   "source": [
    "接下来，我们使用 SVM 算法建模，并使用上面划分的数据训练和测试模型。 \n",
    "\n",
    "Exercise 18.3 \n",
    "\n",
    "挑战：使用 scikit-learn 提供的支持向量机分类方法完成建模，并得到模型在测试集上的准确度结果。 \n",
    "\n",
    "规定：支持向量机分类器参数 ` C=10  ` , ` gamma=0.001  ` ，其余为默认参数。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b889502a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## 代码开始 ### (≈4 行代码)\n",
    "model = None\n",
    "## 代码结束 ###"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab5041a4",
   "metadata": {
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   "source": [
    "参考答案  Exercise 18.3 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb8994cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8296296296296296"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 代码开始 ### (≈4 行代码)\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "model = SVC(C=10, gamma=0.001)\n",
    "model.fit(X_train, y_train)\n",
    "model.score(X_test, y_test)\n",
    "### 代码结束 ###"
   ]
  }
 ],
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